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<div class="title">NEWinogradLayerKernel.h</div>  </div>
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<a href="_n_e_winograd_layer_kernel_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#ifndef __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#define __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_i_n_e_kernel_8h.xhtml">arm_compute/core/NEON/INEKernel.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#include &quot;arm_compute/core/NEON/kernels/convolution/common/convolution.hpp&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &quot;arm_compute/core/NEON/kernels/convolution/common/tensor.hpp&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &quot;arm_compute/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp&quot;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &quot;arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp&quot;</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;{</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="keyword">class </span>ITensor;</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml">   38</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml">INEWinogradLayerTransformInputKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml">INEKernel</a></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;{</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a8002726bde2404f3594fd6f1febc5eed">get_input_storage_size</a>(<span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> n_channels, <span class="keywordtype">int</span> n_rows, <span class="keywordtype">int</span> n_cols, <span class="keywordtype">bool</span> same_padding) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;input_shape, <span class="keyword">const</span> PaddingType padding_type) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(<span class="keyword">const</span> T *<span class="keyword">const</span> input, <span class="keyword">const</span> <span class="keywordtype">int</span> n_batches, <span class="keyword">const</span> <span class="keywordtype">int</span> n_rows, <span class="keyword">const</span> <span class="keywordtype">int</span> n_cols, <span class="keyword">const</span> <span class="keywordtype">int</span> n_channels, <span class="keyword">const</span> PaddingType <a class="code" href="hwc_8hpp.xhtml#ad487ce76e40f524a7f5e350763b91ae5">padding</a>, T *<span class="keyword">const</span> output, <span class="keyword">const</span> <span class="keywordtype">int</span> matrix_stride) = 0;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a57db7dcc6716d3a676a422bb2839036f">   75</a></span>&#160;    <span class="keyword">virtual</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a57db7dcc6716d3a676a422bb2839036f">~INEWinogradLayerTransformInputKernel</a>()</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    {</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    }</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;};</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keywordtype">int</span> OutputTileRows, <span class="keywordtype">int</span> OutputTileCols, <span class="keywordtype">int</span> KernelRows, <span class="keywordtype">int</span> KernelCols&gt;</div><div class="line"><a name="l00081"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml">   81</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml">NEWinogradLayerTransformInputKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml">INEWinogradLayerTransformInputKernel</a>&lt;T&gt;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;{</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a8002726bde2404f3594fd6f1febc5eed">get_input_storage_size</a>(</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        <span class="keywordtype">int</span>  n_batches,</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="keywordtype">int</span>  n_channels,</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        <span class="keywordtype">int</span>  n_rows,</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        <span class="keywordtype">int</span>  n_cols,</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        <span class="keywordtype">bool</span> same_padding) <span class="keyword">const override</span>;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;input_shape, <span class="keyword">const</span> PaddingType padding_type) <span class="keyword">const override</span>;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml">NEWinogradLayerTransformInputKernel</a>();</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">  112</a></span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span> *<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">name</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        <span class="keywordflow">return</span> <span class="stringliteral">&quot;NEWinogradLayerTransformInputKernel&quot;</span>;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    }</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <span class="keyword">const</span> T *<span class="keyword">const</span>    input,</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>         n_batches,</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>         n_rows,</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>         n_cols,</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>         n_channels,</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        <span class="keyword">const</span> PaddingType <a class="code" href="hwc_8hpp.xhtml#ad487ce76e40f524a7f5e350763b91ae5">padding</a>,</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        T *<span class="keyword">const</span>          output,</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>         matrix_stride) <span class="keyword">override</span>;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="comment">// Inherited methods overridden:</span></div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml#af814ff5e96f40f1cccf809b2b4ee19ef">run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &amp;<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#a3f5646133956f06348b310ccc3d36353">window</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#abfab8f0d4928e1081d9f65b77933e24a">is_parallelisable</a>() <span class="keyword">const override</span>;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#aa223aca04dd789990b8422db5631fe61">  142</a></span>&#160;    <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#aa223aca04dd789990b8422db5631fe61">WinogradBase</a> = winograd::WinogradGEMM&lt;OutputTileRows, OutputTileCols, KernelCols, KernelCols&gt;;</div><div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#af17b2792d63e38a0eea385635191e836">  143</a></span>&#160;    <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#af17b2792d63e38a0eea385635191e836">WinogradConv</a> = <span class="keyword">typename</span> WinogradBase::template Convolution&lt;T, T&gt;;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    <span class="keyword">using</span> InputTransform = <span class="keyword">typename</span> WinogradBase::template InputTransform&lt;T&gt;;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    std::unique_ptr&lt;InputTransform&gt; _transform;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;};</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00151"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml">  151</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml">INEWinogradLayerTransformOutputKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml">INEKernel</a></div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;{</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> get_output_storage_size(<span class="keywordtype">int</span> n_batches, <span class="keywordtype">int</span> n_rows, <span class="keywordtype">int</span> n_cols, <span class="keywordtype">int</span> n_output_channels, <span class="keywordtype">bool</span> same_padding) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;input_shape, <span class="keyword">const</span> PaddingType padding_type) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keyword">virtual</span> Tensor4DShape get_output_shape(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;in_shape, <span class="keyword">const</span> PaddingType <a class="code" href="hwc_8hpp.xhtml#ad487ce76e40f524a7f5e350763b91ae5">padding</a>) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases,</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;        <span class="keyword">const</span> T *<span class="keyword">const</span> output_workingspace,</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      matrix_stride,</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;        T *<span class="keyword">const</span>       output,</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_batches,</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_rows,</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_cols,</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_channels) = 0;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml#a0bda64948b232256769698cde90fcdb0">  206</a></span>&#160;    <span class="keyword">virtual</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml#a0bda64948b232256769698cde90fcdb0">~INEWinogradLayerTransformOutputKernel</a>()</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    {</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    }</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;};</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keywordtype">int</span> OutputTileRows, <span class="keywordtype">int</span> OutputTileCols, <span class="keywordtype">int</span> KernelRows, <span class="keywordtype">int</span> KernelCols&gt;</div><div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">  212</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml">INEWinogradLayerTransformOutputKernel</a>&lt;T&gt;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;{</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00215"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">  215</a></span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span> *<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">name</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        <span class="keywordflow">return</span> <span class="stringliteral">&quot;NEWinogradLayerTransformOutputKernel&quot;</span>;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    }</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a>();</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a> &amp;) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a> &amp;operator=(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a> &amp;) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a>(<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">NEWinogradLayerTransformOutputKernel</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160; 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   <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;input_shape, <span class="keyword">const</span> PaddingType padding_type) <span class="keyword">const override</span>;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;    Tensor4DShape get_output_shape(<span class="keyword">const</span> KernelShape &amp;kernel_shape, <span class="keyword">const</span> Tensor4DShape &amp;in_shape, <span class="keyword">const</span> PaddingType <a class="code" href="hwc_8hpp.xhtml#ad487ce76e40f524a7f5e350763b91ae5">padding</a>) <span class="keyword">const override</span>;</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *biases,</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        <span class="keyword">const</span> T *<span class="keyword">const</span> output_workingspace,</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      matrix_stride,</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        T *<span class="keyword">const</span>       output,</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_batches,</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_rows,</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_cols,</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>      n_channels) <span class="keyword">override</span>;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml#af814ff5e96f40f1cccf809b2b4ee19ef">run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &amp;<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#a3f5646133956f06348b310ccc3d36353">window</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#abfab8f0d4928e1081d9f65b77933e24a">is_parallelisable</a>() <span class="keyword">const override</span>;</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="keyword">using</span> WinogradBase    = winograd::WinogradGEMM&lt;OutputTileRows, OutputTileCols, KernelRows, KernelCols&gt;;</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="keyword">using</span> WinogradConv    = <span class="keyword">typename</span> WinogradBase::template Convolution&lt;T, T&gt;;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <span class="keyword">using</span> OutputTransform = <span class="keyword">typename</span> WinogradBase::template OutputTransform&lt;T&gt;;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *_biases;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    <span class="keyword">const</span> T       *_output_workspace;</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;    <span class="keywordtype">int</span>            _matrix_stride;</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="keywordtype">int</span>            _matrix_row_stride;</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    T             *_output;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    <span class="keywordtype">int</span>            _n_batches;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keywordtype">int</span>            _n_rows;</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <span class="keywordtype">int</span>            _n_cols;</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <span class="keywordtype">int</span>            _n_channels;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;};</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div><div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml">  305</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml">INEWinogradLayerTransformWeightsKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml">INEKernel</a></div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;{</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> get_weight_storage_size(<span class="keywordtype">int</span> n_output_channels, <span class="keywordtype">int</span> n_input_channels) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape) <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights_hwio, T *<span class="keyword">const</span> output, <span class="keyword">const</span> <span class="keywordtype">int</span> matrix_stride, <span class="keyword">const</span> <span class="keywordtype">int</span> n_output_channels, <span class="keyword">const</span> <span class="keywordtype">int</span> n_input_channels) = 0;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;</div><div class="line"><a name="l00333"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml#a668729f636910fd5f425cee965efee6d">  333</a></span>&#160;    <span class="keyword">virtual</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml#a668729f636910fd5f425cee965efee6d">~INEWinogradLayerTransformWeightsKernel</a>()</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    {</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    }</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;};</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keywordtype">int</span> OutputTileRows, <span class="keywordtype">int</span> OutputTileCols, <span class="keywordtype">int</span> KernelRows, <span class="keywordtype">int</span> KernelCols&gt;</div><div class="line"><a name="l00339"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml">  339</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml">NEWinogradLayerTransformWeightsKernel</a> final : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml">INEWinogradLayerTransformWeightsKernel</a>&lt;T&gt;</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;{</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml">NEWinogradLayerTransformWeightsKernel</a>();</div><div class="line"><a name="l00343"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">  343</a></span>&#160;    <span class="keyword">const</span> <span class="keywordtype">char</span> *<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">name</a>()<span class="keyword"> const override</span></div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;        <span class="keywordflow">return</span> <span class="stringliteral">&quot;NEWinogradLayerTransformWeightsKernel&quot;</span>;</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    }</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    <span class="comment">// Inherited methods overridden:</span></div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *weights_hwio, T *<span class="keyword">const</span> output, <span class="keyword">const</span> <span class="keywordtype">int</span> matrix_stride, <span class="keyword">const</span> <span class="keywordtype">int</span> n_output_channels, <span class="keyword">const</span> <span class="keywordtype">int</span> n_input_channels) <span class="keyword">override</span>;</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> get_weight_storage_size(<span class="keywordtype">int</span> n_output_channels, <span class="keywordtype">int</span> n_input_channels) <span class="keyword">const override</span>;</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">get_matrix_stride</a>(<span class="keyword">const</span> KernelShape &amp;kernel_shape) <span class="keyword">const override</span>;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml#af814ff5e96f40f1cccf809b2b4ee19ef">run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &amp;<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#a3f5646133956f06348b310ccc3d36353">window</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;    <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#abfab8f0d4928e1081d9f65b77933e24a">is_parallelisable</a>() <span class="keyword">const override</span>;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    <span class="keyword">using</span> WinogradBase     = winograd::WinogradGEMM&lt;OutputTileRows, OutputTileCols, KernelRows, KernelCols&gt;;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    <span class="keyword">using</span> WinogradConv     = <span class="keyword">typename</span> WinogradBase::template Convolution&lt;T, T&gt;;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <span class="keyword">using</span> WeightsTransform = <span class="keyword">typename</span> WinogradBase::template WeightsTransform&lt;T&gt;;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    std::unique_ptr&lt;WeightsTransform&gt; _transform;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;};</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> TIn, <span class="keyword">typename</span> TOut&gt;</div><div class="line"><a name="l00363"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_i_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">  363</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">INEWinogradLayerBatchedGEMMKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml">INEKernel</a></div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;{</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> get_number_gemms() <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n_gemms,</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> K, <span class="keyword">const</span> <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        a_matrix_stride,</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        a_row_stride,</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        b_matrix_stride,</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        b_row_stride,</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        c_matrix_stride,</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        c_row_stride,</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        <span class="keyword">const</span> TIn *<span class="keyword">const</span> a_ptr,</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;        <span class="keyword">const</span> TIn *<span class="keyword">const</span> b_ptr,</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;        TOut *<span class="keyword">const</span>      c_ptr) = 0;</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> get_output_tile_rows() <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> get_output_tile_cols() <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    <span class="keyword">virtual</span> <span class="keywordtype">int</span> get_number_blocks() <span class="keyword">const</span> = 0;</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;};</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> TIn, <span class="keyword">typename</span> TOut, <span class="keywordtype">int</span> OutputTileRows, <span class="keywordtype">int</span> OutputTileCols, <span class="keywordtype">int</span> KernelRows, <span class="keywordtype">int</span> KernelCols&gt;</div><div class="line"><a name="l00410"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">  410</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">INEWinogradLayerBatchedGEMMKernel</a>&lt;TIn, TOut&gt;</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;{</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00413"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#aa2895a99d9a1efae7844264931a0d348">  413</a></span>&#160;    <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#aa2895a99d9a1efae7844264931a0d348">WinogradBase</a> = winograd::WinogradGEMM&lt;OutputTileRows, OutputTileCols, KernelRows, KernelCols&gt;;</div><div class="line"><a name="l00414"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#acd8461f7d784d4c09b73447f357e6b1a">  414</a></span>&#160;    <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#acd8461f7d784d4c09b73447f357e6b1a">WinogradConv</a> = <span class="keyword">typename</span> WinogradBase::template Convolution&lt;TIn, TOut&gt;;</div><div class="line"><a name="l00415"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#a14ba69200a7c35d6bc6f12734c26775c">  415</a></span>&#160;    <span class="keyword">using</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#a14ba69200a7c35d6bc6f12734c26775c">MultiGEMM</a>    = winograd::BatchedBlockedGemm&lt;WinogradConv::M_BLOCK, WinogradConv::N_BLOCK, TIn, TOut&gt;;</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;</div><div class="line"><a name="l00417"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#a418b17d4e8cf867d837ce92b9919241a">  417</a></span>&#160; 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   <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> &amp;operator=(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> &amp;) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a>(<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;    <a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> &amp;operator=(<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a> &amp;&amp;) = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    ~<a class="code" href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">NEWinogradLayerBatchedGEMMKernel</a>() = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    <span class="comment">// Inherited methods overridden:</span></div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> get_number_gemms() <span class="keyword">const override</span>;</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;    <span class="keywordtype">int</span>          get_output_tile_rows() <span class="keyword">const override</span>;</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;    <span class="keywordtype">int</span>          get_output_tile_cols() <span class="keyword">const override</span>;</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;    <span class="keywordtype">int</span>          get_number_blocks() <span class="keyword">const override</span>;</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">configure</a>(</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n_gemms,</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> K, <span class="keyword">const</span> <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        a_matrix_stride,</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        a_row_stride,</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        b_matrix_stride,</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        b_row_stride,</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        c_matrix_stride,</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>        c_row_stride,</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        <span class="keyword">const</span> TIn *<span class="keyword">const</span> a_ptr,</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        <span class="keyword">const</span> TIn *<span class="keyword">const</span> b_ptr,</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        TOut *<span class="keyword">const</span>      c_ptr) <span class="keyword">override</span>;</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_i_c_p_p_kernel.xhtml#af814ff5e96f40f1cccf809b2b4ee19ef">run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &amp;<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#a3f5646133956f06348b310ccc3d36353">window</a>, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>) <span class="keyword">override</span>;</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    std::unique_ptr&lt;MultiGEMM&gt; _gemms;</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;};</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/*__ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__*/</span><span class="preprocessor"></span></div><div class="ttc" id="classarm__compute_1_1_i_c_p_p_kernel_xhtml_af814ff5e96f40f1cccf809b2b4ee19ef"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_kernel.xhtml#af814ff5e96f40f1cccf809b2b4ee19ef">arm_compute::ICPPKernel::run</a></div><div class="ttdeci">virtual void run(const Window &amp;window, const ThreadInfo &amp;info)=0</div><div class="ttdoc">Execute the kernel on the passed window. </div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml">arm_compute::NEWinogradLayerTransformOutputKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00212">NEWinogradLayerKernel.h:212</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml_ab5656bb5b6334bdbe6e606c715872828"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">arm_compute::NEWinogradLayerBatchedGEMMKernel::name</a></div><div class="ttdeci">const char * name() const override</div><div class="ttdoc">Name of the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00420">NEWinogradLayerKernel.h:420</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel_xhtml_a2ac7c7acdb2d7dea85085c2dff52687f"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a2ac7c7acdb2d7dea85085c2dff52687f">arm_compute::INEWinogradLayerTransformInputKernel::configure</a></div><div class="ttdeci">virtual void configure(const T *const input, const int n_batches, const int n_rows, const int n_cols, const int n_channels, const PaddingType padding, T *const output, const int matrix_stride)=0</div><div class="ttdoc">Configure the output transform kernel. </div></div>
<div class="ttc" id="classarm__compute_1_1_i_c_p_p_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_c_p_p_kernel.xhtml">arm_compute::ICPPKernel</a></div><div class="ttdoc">Common interface for all kernels implemented in C++. </div><div class="ttdef"><b>Definition:</b> <a href="_i_c_p_p_kernel_8h_source.xhtml#l00035">ICPPKernel.h:35</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">arm_compute::NEWinogradLayerBatchedGEMMKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00410">NEWinogradLayerKernel.h:410</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel_xhtml_ab5656bb5b6334bdbe6e606c715872828"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">arm_compute::NEWinogradLayerTransformWeightsKernel::name</a></div><div class="ttdeci">const char * name() const override</div><div class="ttdoc">Name of the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00343">NEWinogradLayerKernel.h:343</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml_aa2895a99d9a1efae7844264931a0d348"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#aa2895a99d9a1efae7844264931a0d348">arm_compute::NEWinogradLayerBatchedGEMMKernel::WinogradBase</a></div><div class="ttdeci">winograd::WinogradGEMM&lt; OutputTileRows, OutputTileCols, KernelRows, KernelCols &gt; WinogradBase</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00413">NEWinogradLayerKernel.h:413</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel_xhtml_a0bda64948b232256769698cde90fcdb0"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml#a0bda64948b232256769698cde90fcdb0">arm_compute::INEWinogradLayerTransformOutputKernel::~INEWinogradLayerTransformOutputKernel</a></div><div class="ttdeci">virtual ~INEWinogradLayerTransformOutputKernel()</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00206">NEWinogradLayerKernel.h:206</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a096668313a9a819d54a2e65ec21ff0cc"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">arm_compute::test::validation::info</a></div><div class="ttdeci">src info() -&gt; set_format(Format::S16)</div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml">arm_compute::INEWinogradLayerTransformWeightsKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00305">NEWinogradLayerKernel.h:305</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel_xhtml_aa223aca04dd789990b8422db5631fe61"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#aa223aca04dd789990b8422db5631fe61">arm_compute::NEWinogradLayerTransformInputKernel::WinogradBase</a></div><div class="ttdeci">winograd::WinogradGEMM&lt; OutputTileRows, OutputTileCols, KernelCols, KernelCols &gt; WinogradBase</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00142">NEWinogradLayerKernel.h:142</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_kernel_xhtml_abfab8f0d4928e1081d9f65b77933e24a"><div class="ttname"><a href="classarm__compute_1_1_i_kernel.xhtml#abfab8f0d4928e1081d9f65b77933e24a">arm_compute::IKernel::is_parallelisable</a></div><div class="ttdeci">virtual bool is_parallelisable() const </div><div class="ttdoc">Indicates whether or not the kernel is parallelisable. </div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel_xhtml_ab5656bb5b6334bdbe6e606c715872828"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_output_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">arm_compute::NEWinogradLayerTransformOutputKernel::name</a></div><div class="ttdeci">const char * name() const override</div><div class="ttdoc">Name of the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00215">NEWinogradLayerKernel.h:215</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel_xhtml_a668729f636910fd5f425cee965efee6d"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_weights_kernel.xhtml#a668729f636910fd5f425cee965efee6d">arm_compute::INEWinogradLayerTransformWeightsKernel::~INEWinogradLayerTransformWeightsKernel</a></div><div class="ttdeci">virtual ~INEWinogradLayerTransformWeightsKernel()</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00333">NEWinogradLayerKernel.h:333</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml">arm_compute::NEWinogradLayerTransformInputKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00081">NEWinogradLayerKernel.h:81</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel_xhtml_a57db7dcc6716d3a676a422bb2839036f"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a57db7dcc6716d3a676a422bb2839036f">arm_compute::INEWinogradLayerTransformInputKernel::~INEWinogradLayerTransformInputKernel</a></div><div class="ttdeci">virtual ~INEWinogradLayerTransformInputKernel()</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00075">NEWinogradLayerKernel.h:75</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel_xhtml_ae25b6ed77179808984b17c39e078ad96"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#ae25b6ed77179808984b17c39e078ad96">arm_compute::INEWinogradLayerTransformInputKernel::get_matrix_stride</a></div><div class="ttdeci">virtual int get_matrix_stride(const KernelShape &amp;kernel_shape, const Tensor4DShape &amp;input_shape, const PaddingType padding_type) const =0</div><div class="ttdoc">Gets the stride between matrices in the input worspace. </div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml_a14ba69200a7c35d6bc6f12734c26775c"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#a14ba69200a7c35d6bc6f12734c26775c">arm_compute::NEWinogradLayerBatchedGEMMKernel::MultiGEMM</a></div><div class="ttdeci">winograd::BatchedBlockedGemm&lt; WinogradConv::M_BLOCK, WinogradConv::N_BLOCK, TIn, TOut &gt; MultiGEMM</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00415">NEWinogradLayerKernel.h:415</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_output_kernel.xhtml">arm_compute::INEWinogradLayerTransformOutputKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00151">NEWinogradLayerKernel.h:151</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_weights_kernel.xhtml">arm_compute::NEWinogradLayerTransformWeightsKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00339">NEWinogradLayerKernel.h:339</a></div></div>
<div class="ttc" id="hwc_8hpp_xhtml_ad487ce76e40f524a7f5e350763b91ae5"><div class="ttname"><a href="hwc_8hpp.xhtml#ad487ce76e40f524a7f5e350763b91ae5">padding</a></div><div class="ttdeci">uint8_t padding[4]</div><div class="ttdef"><b>Definition:</b> <a href="hwc_8hpp_source.xhtml#l00273">hwc.hpp:273</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel_xhtml_a8002726bde2404f3594fd6f1febc5eed"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml#a8002726bde2404f3594fd6f1febc5eed">arm_compute::INEWinogradLayerTransformInputKernel::get_input_storage_size</a></div><div class="ttdeci">virtual unsigned int get_input_storage_size(int n_batches, int n_channels, int n_rows, int n_cols, bool same_padding) const =0</div><div class="ttdoc">Determine how much memory (in units of TIn) to allocate for the transformed input. </div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel_xhtml_af17b2792d63e38a0eea385635191e836"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#af17b2792d63e38a0eea385635191e836">arm_compute::NEWinogradLayerTransformInputKernel::WinogradConv</a></div><div class="ttdeci">typename WinogradBase::template Convolution&lt; T, T &gt; WinogradConv</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00143">NEWinogradLayerKernel.h:143</a></div></div>
<div class="ttc" id="structarm__compute_1_1_thread_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_thread_info.xhtml">arm_compute::ThreadInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_types_8h_source.xhtml#l00058">CPPTypes.h:58</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel_xhtml_ab5656bb5b6334bdbe6e606c715872828"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_transform_input_kernel.xhtml#ab5656bb5b6334bdbe6e606c715872828">arm_compute::NEWinogradLayerTransformInputKernel::name</a></div><div class="ttdeci">const char * name() const override</div><div class="ttdoc">Name of the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00112">NEWinogradLayerKernel.h:112</a></div></div>
<div class="ttc" id="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml_acd8461f7d784d4c09b73447f357e6b1a"><div class="ttname"><a href="classarm__compute_1_1_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml#acd8461f7d784d4c09b73447f357e6b1a">arm_compute::NEWinogradLayerBatchedGEMMKernel::WinogradConv</a></div><div class="ttdeci">typename WinogradBase::template Convolution&lt; TIn, TOut &gt; WinogradConv</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00414">NEWinogradLayerKernel.h:414</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_transform_input_kernel.xhtml">arm_compute::INEWinogradLayerTransformInputKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00038">NEWinogradLayerKernel.h:38</a></div></div>
<div class="ttc" id="_i_n_e_kernel_8h_xhtml"><div class="ttname"><a href="_i_n_e_kernel_8h.xhtml">INEKernel.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_kernel_xhtml_a3f5646133956f06348b310ccc3d36353"><div class="ttname"><a href="classarm__compute_1_1_i_kernel.xhtml#a3f5646133956f06348b310ccc3d36353">arm_compute::IKernel::window</a></div><div class="ttdeci">const Window &amp; window() const </div><div class="ttdoc">The maximum window the kernel can be executed on. </div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_n_e_winograd_layer_batched_g_e_m_m_kernel_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_n_e_winograd_layer_batched_g_e_m_m_kernel.xhtml">arm_compute::INEWinogradLayerBatchedGEMMKernel</a></div><div class="ttdef"><b>Definition:</b> <a href="_n_e_winograd_layer_kernel_8h_source.xhtml#l00363">NEWinogradLayerKernel.h:363</a></div></div>
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